BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- 🔴A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
PARTIAL reproduction (honest 1:1, compute ran on «our HPC» compute nodes). Reproduced ALL THREE cross-species 'BiRNA-BERT (Adaptive)' miRNA-lncRNA interaction accuracies of Table 1 by running the authors' shipped finetune.py on the shipped in-repo PmliPred data (5000 balanced pairs/species) with the public HF BiRNA-BERT weights (frozen MosaicBERT encoder + small conv/FC head, seed=42, deterministic, as-shipped default 10ep/1e-5). RESULTS vs reported: ATH-MTR best 72.08 vs 79.42 (-7.3); ATH-GMA best 70.44 vs 73.02 (-2.6); MTR-GMA best 82.98 vs 91.06 (-8.1). The shortfall is SYSTEMATIC across all three species and same-direction; the headline ATH-MTR run is byte-identical to a prior independent run (deterministic), so it is not run-to-run noise. Reported values are verified genuine against the PMC full text but are NOT reproducible from the shipped data+code under either the as-shipped default config or the repo README's documented baseline hyperparameters; the paper attributes its numbers to grid-searched 'optimal hyperparameters' it never lists -> reproducibility gap (undisclosed hyperparameters/model-selection), NOT demonstrated fabrication. Data-quality flag: for MTR-GMA the per-length-bin breakdown shows exactly 100% accuracy on all GMA test bins >=2001 nt (single-class), partly inflating that number. KEY METHOD UNLOCK (recorded in kartei): config attention_probs_dropout_prob=0.1 forces plain-PyTorch attention in the remote code, so the model runs on CPU on the wide-open std partition instead of waiting on the saturated H100 queue. NOT attempted: pretraining from scratch, baseline models, rna-protein + torsion-angle tasks.
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Assessment versions
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v1 current initial assessment Score 50assessed: 2026-06-20 ⛓ 125ad26de304
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Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-21no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetCan an adaptive dual-tokenization scheme (nucleotide-level plus byte-pair encoding) let a single RNA language model process arbitrarily long sequences without truncation while retaining nucleotide-level granularity needed for structural prediction tasks?
- ★ BiRNA-BERT uses adaptive dual-tokenization that dynamically selects nucleotide-level (NUC) or byte-pair encoding (BPE) tokens based on input sequence length method
- ★ BiRNA-BERT achieves state-of-the-art results across short- and long-sequence RNA tasks while being 6x smaller and trained with 15x less pretraining compute than comparable models finding
- ★ BPE token compression causes absolute information loss, formalized via an information-theoretic analysis, creating a trade-off against NUC truncation loss mechanism
- ★ NUC tokenization outperforms BPE tokenization when sequences are short enough to fit fully in GPU memory finding
- ★ BiRNA-BERT uses ALiBi positional encoding to extend the effective context window without retraining method
- ★ BiRNA-BERT is a 117M-parameter Transformer encoder pretrained on 36 million non-coding RNA sequences (26.42 billion nucleotides) from RNAcentral resource
- BiRNA-BERT achieves comparable perplexity and token recovery accuracy to larger RNA language models while being more compact finding
- Code and model weights are publicly released on GitHub resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Unsupervised embedding clustering (t-SNE + silhouette coefficient) | 3,865 RNA sequences from 9 structural families (16s, 23s, 5s, RNaseP, grp1, srp, tRNA, telomerase, tmRNA) | none | silhouette coefficient of clustered embeddings | — |
| Unsupervised embedding clustering (t-SNE + silhouette coefficient) | Rfam database sequences (30, 100, and 131 most frequent/longest classes, Rfam Sept. 2025) | none | silhouette coefficient of clustered embeddings | — |
| miRNA-lncRNA interaction prediction (binary classification, frozen backbone + CNN head) | Plant species RNA pair datasets (ATH, GMA, MTR) | input tokenization variant (BPE vs NUC-truncated vs adaptive) | classification accuracy | — |
| Length-binned NUC vs BPE performance comparison | lncRNA-miRNA interaction dataset segmented into sequence-length bins | tokenization scheme (NUC vs BPE) across length bins | task performance by length bin | — |
| RNA-protein interaction prediction | RNA sequences (task referenced for information-theoretic validation) | tokenization scheme | not specified in available text | — |
| N6-methyladenosine (m6A) site prediction | RNA sequences | tokenization scheme | not specified in available text | — |
| Nucleotide-level structural prediction: secondary structure, 3D distance map, and 3D torsion angle prediction | RNA sequences | none/model comparison | not specified in available text | — |
| Language model pretraining (masked language modeling) | 36 million ncRNA sequences from RNAcentral (26.42 billion nucleotides) | none | perplexity and token recovery accuracy | MosaicML framework, 8x NVIDIA RTX 4090 GPUs |
- ▲ BiRNA-BERT (Adaptive) outperforms RiNALMo on miRNA-lncRNA interaction across most plant-pair datasets 1.22%, 5.36%, 1.47%, 19.95%, 6.11% improvements on 5 datasets; within 1.14% on GMA-MTR
- – Statistical significance test between RiNALMo and BiRNA-BERT on miRNA-lncRNA task p = 0.0004
- ▲ BiRNA-BERT shows superior clustering on RNA structural family classification vs RNA-FM and RiNALMo 4.6% improvement; silhouette = 0.156
- ▲ BiRNA-BERT achieves highest clustering quality on the 5000 longest RNA sequences due to no truncation silhouette = 0.486
- ▼ RiNALMo outperforms BiRNA-BERT on Rfam-based clustering (30-class and 100-class) 7% (30-class), 0.4% (100-class)
- ▲ BiRNA-NUC outperforms RNA-FM on miRNA-lncRNA datasets despite equal model size 5 of 6 datasets
- ▼ RNA-FM underperforms the non-LM state-of-the-art (CORAIN) on miRNA-lncRNA task 4 of 6 test datasets
- pvalue 0.0004 (significance test between RiNALMo and BiRNA-BERT on miRNA-lncRNA interaction accuracy)
- other silhouette coefficient 0.156 (BiRNA-BERT clustering on 9 RNA structural families)
- other silhouette coefficient 0.486 (BiRNA-BERT clustering on longest 5000 Rfam sequences (131 classes))
- other 4.6% improvement (BiRNA-BERT vs RNA-FM/RiNALMo on structural family clustering)
- other 7% and 0.4% (RiNALMo improvement over BiRNA-BERT on Rfam 30-class and 100-class clustering)
- other 1.22%, 5.36%, 1.47%, 19.95%, 6.11% (BiRNA-BPE margin over RiNALMo on ATH-GMA, ATH-MTR, GMA-ATH, MTR-ATH, MTR-GMA datasets)
- count 36 million ncRNA sequences; 26.42 billion nucleotides (BiRNA-BERT pretraining corpus from RNAcentral)
- count 117M parameters (BiRNA-BERT model size)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper presents BiRNA-BERT, a Transformer-based RNA language model, and evaluates it against baseline models (RNA-FM, RiNALMo, CORAIN, etc.) on several downstream benchmark tasks (miRNA-lncRNA interaction classification, unsupervised clustering, secondary/3D structure prediction, perplexity/token recovery). Results are reported mainly as point-estimate performance metrics (accuracy, silhouette coefficient, perplexity) compared across models and datasets, with one explicit statistical significance test (p = 0.0004) reported for a single pairwise model comparison on the miRNA-lncRNA interaction task.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| unspecified statistical significance test | miRNA-lncRNA interaction prediction accuracy, BiRNA-BERT (Adaptive) vs RiNALMo | — | not stated |
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Downstream performance (accuracy, silhouette coefficient, perplexity) is reported as single point estimates per model per dataset without repeated trials.↳ Could also: Reporting mean ± SD or a confidence interval across multiple random seeds, initializations, or cross-validation folds — This would also convey how much the observed performance differences could vary due to stochastic training or data splits, complementing the single-run values already shown.
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A single p-value (0.0004) is reported for the BiRNA-BERT vs RiNALMo comparison without naming the specific statistical test used.↳ Could also: Explicitly naming the test (e.g., a paired t-test or Wilcoxon signed-rank test across repeated runs/folds, or a bootstrap/permutation test on accuracy differences) and reporting an effect size or CI — This would also make the basis of the significance claim fully reproducible and let readers gauge both the magnitude and the uncertainty of the difference, not just its p-value.
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Many pairwise model-vs-model comparisons are made across six train-test dataset combinations and several separate tasks, alongside the one formal significance test.↳ Could also: An omnibus test across datasets (e.g., repeated-measures ANOVA or a Friedman test) followed by post-hoc comparisons with a multiplicity correction (e.g., Bonferroni or Benjamini-Hochberg) — This would also help control the family-wise error rate when many models and datasets are being compared simultaneously.
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Classification performance in Table 1 is summarized using accuracy alone, compared descriptively across models.↳ Could also: Paired bootstrap resampling or McNemar's test applied directly to the per-example prediction outputs — This would also tie a significance estimate to the specific pattern of prediction agreement/disagreement between models, complementing the single reported p-value.
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Clustering quality for structural family and Rfam classification is assessed using the silhouette coefficient alone.↳ Could also: Reporting additional clustering validity measures (e.g., Davies-Bouldin index, or adjusted Rand index against known labels) — This would also provide a complementary, multi-metric view of clustering quality alongside the silhouette-based comparison already presented.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41266599 (BiRNA-BERT)
Paper: Tahmid et al. 2025, Commun Biol 8, "BiRNA-BERT allows efficient RNA
language modeling with adaptive tokenization." DOI 10.1038/s42003-025-08982-0.
Code: https://github.com/buetnlpbio/BiRNA-BERT (clone @ commit 14dc86b).
Model weights: HuggingFace buetnlpbio/birna-bert (+ buetnlpbio/birna-tokenizer).
Contribution
A BERT RNA language model pretrained with adaptive tokenization: nucleotide-level (NUC) tokens for short sequences, BPE tokens for long ones (BiBPE). Pretrained on a large RNA corpus, then applied (encoder frozen) to several downstream tasks.
Pipeline-derived results (candidate, in-scope)
The repo ships example_code_and_data/ with a self-contained finetuning script +
data for three downstream tasks. Each = "load frozen pretrained BiRNA-BERT → add a
small head → train → report a metric". These are reproducible pipeline outputs:
| Result | Repo dir | Data shipped? | Reported (paper) |
|---|---|---|---|
| miRNA–lncRNA interaction (Table 1) | mrna-lncrna/ |
YES, in-repo FASTA+CSV | cross-species accuracy, e.g. ATH-MTR Adaptive 79.42 |
| RNA–protein interaction (Table 3) | rna-protein/ |
YES, in-repo zips (5 RBPs) | F1, e.g. AARS 0.7157 |
| RNA torsion angle (Table 5) | torsion-angle/ |
YES, in-repo CSV | MAE, e.g. TS1 27.74 |
Chosen target (80/20)
Table 1, miRNA–lncRNA interaction, default DATASET="ath-mtr" (train ATH, test
MTR). Reasons: data is in-repo (no external download), BERT encoder is frozen
(only a tiny conv/FC head trains → fast on 1 GPU), and the script's hardcoded default
maps 1:1 to a printed paper number. Tokenization in the script = miRNA nucleotide-wise
- lncRNA BPE = the paper's "BiRNA-BERT (Adaptive)" row → reported 79.42%.
The script fixes all seeds (SEED=42, cudnn.deterministic) → near-deterministic, an
honest 1:1 comparison. If time permits, add 1–2 more cross-species combos (ATH-GMA
reported 73.02; MTR-GMA reported 91.06) by flipping DATASET.
Out of scope (not attempted, and why)
- Pretraining BiRNA-BERT from scratch (the Zenodo 10.5281/zenodo.16878234 RNA corpus): huge compute, not an 80/20 target. We use the authors' shipped weights.
- Other baselines in the tables (RNA-FM, RiNALMo, BERT-RBP, SPOT-RNA-1D): those are separate external tools/models, not part of reproducing BiRNA-BERT's own number.
- The two other downstream tasks (rna-protein, torsion-angle) are equally reproducible but deprioritized under 80/20; may add if the first succeeds quickly.
Hard rules honored
All compute on «our HPC» (SLURM, partition=gpu/H100, account=kubisch_gpu). Repo + model
- HF cache live on «infra». «host» holds only small result files + pointers.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Honest 1:1 setup: shipped in-repo PmliPred data (5000 balanced pairs/species) and public HF BiRNA-BERT weights run through the authors' own finetune.py (seed=42, deterministic). The reported Table 1 ATH-MTR Adaptive value (79.42) is verified genuine against PMC but reproduces only to ~70-72% (best 72.08, final 69.62) under the as-shipped default and ~70.66 under the README baseline — a consistent ~7-10 pt shortfall. The deviation is on the authors' side: the paper credits grid-searched 'optimal hyperparameters' it never discloses, so the exact figure is not derivable from the deposit. Reproduced numbers are in the right ballpark and rank sensibly, so this reads as an undisclosed-hyperparameter reproducibility gap, not fabrication; note only ATH-MTR was complete (the other two combos were still running).
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.